Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico

Today’s cyclodextrin:
Check out this fascinating paper guiding us to the future of CDs:

AI-driven cyclodextrin drug design

Fascinating research by Amelia Anderson & Matthew “Oki” O’Connor from Cyclarity Therapeutics and Angel Piñeiro & Rebeca Garcia-Fandino from Universidad de Santiago de Compostela

Computational methods represent an exceptional complement to in vitro assays because they can be employed for existing and hypothetical molecules, providing high resolution structures in addition to a mechanistic, dynamic, kinetic, and thermodynamic characterization.

Bridging computational methods with complex molecular interactions, this research enables predictive CD designs for diverse applications. Moreover, the high reproducibility, sensitivity, and cost-effectiveness of the studied methods pave the way for extensive studies of massive CD-ligand combinations, enabling AI algorithm training and automated molecular design.

Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico – Computational and Structural Biotechnology Journal (csbj.org)